Many oncoproteins and tumor suppressive proteins shuttle between the cytosol and nucleus and thus interact with changing sets of protein partners in different sub-cellular compartments. Furthermore, increasing evidence indicates that oncogenic mutations of these proteins can result in alteration of cellular localization and rewirin of oncoprotein interaction networks. Characterization of the dynamic interaction network of oncoproteins and tumor suppressive proteins is therefore crucial to understand their roles in tumorigenesis. Robust technologies for mapping dynamic protein interaction networks under physiological conditions within the cell are not yet available. We propose to develop a novel approach for mapping dynamic oncoprotein interaction networks using endogenously epitope-tagged proteins for affinity- purification and quantitative proteomics for accurate network identification. This application builds upon our successful development of an innovative method to introduce epitope tag- encoding DNA into endogenous loci by homologous recombination-mediated knock-in in human cancer cells and our intensive experience in quantitative proteomics analysis. The endogenously tagged proteins are expressed at physiological levels and provide physiologically-relevant environments and compartments for protein complex identification and are therefore ideal for mapping dynamic protein networks. The goals of this application are twofold. First, we propose a detailed quantitative comparison of our technique in terms of distinguishing mutant and wild-type oncoprotein complexes against two conventional approaches for studying protein complexes. Second we test the potential of our new approach for studying the dynamic interactions that occur in response to cell signaling. Successful development of this technology will provide a platform for understanding the reconfigurations to protein interaction networks that result from oncogenic related translocation or mutation. As large-scale projects such as The Cancer Genome Atlas (TCGA) proceed, many more novel oncogenes and tumor suppressor genes will be discovered; our technology provides an important pipeline for understanding the function of these genes at the interaction network level by mapping their dynamic interaction networks.